import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from tabulate import tabulate

#Make some functions: 
#Descriptive Statistics Table Maker: 
def table(df):
    statistics = df.describe(percentiles = [.25, .5, .75])
    statistics = statistics.transpose()
    columns = ['mean', '25%', '50%', '75%', 'std', 'min', 'max']
    table = statistics[columns]
    display(table)
    return(table)


#Excel processing
def process(path, tab_name, row_to_skip, column_to_skip):
    df = pd.read_excel(path, sheet_name=tab_name, skiprows=row_to_skip)

    # Set the first row as column names and the first column as the index
    df.columns = df.iloc[0]
    df.index = df.iloc[:, 0]

    # Store the index for later use
    index = df.iloc[:, 0]

    # Remove the first row and the first column, and reset the index
    df = df.iloc[1:].reset_index(drop=True)
    df = df.iloc[:, 1:]

    # Set the index to the values from the stored index
    df.index = index.values[1:]

    df = df.iloc[:, :column_to_skip]

    return df

def heatmap(table):
    plt.figure(figsize=(10, 10))
    mask = np.triu(np.ones_like(table.corr()))
    heatmap = sns.heatmap(table.corr(), mask=mask, annot=True)
    plt.show()


def plot_dataframe(df, title, x_label, y_label):
    for column in df.columns:
        plt.plot(df.index, df[column], label=column)
    # Customize the plot
    plt.title(title)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()
    
def descriptive_stats(group):
    for i in group:
        print("Variable: ", i)        
        names = ["mean", 'std', 'min', '25%', '50%', '75%', 'max']
        values = [group[i].mean(), group[i].std(), group[i].min(), group[i].quantile(q=0.25), group[i].quantile(q=0.50), group[i].quantile(q=0.75), group[i].max()]
        table = [names, values]
        print(tabulate(table))
        print("\n")

#Add the Macro data: 
path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\macro\CPIAUCSL.xls"
CPI = process(path, "FRED Graph", 9, 1)
CPI = CPI[860:]
path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\macro\T10YIE.xls"
T10 = process(path, "FRED Graph", 9, 1)
T10 = T10[T10['T10YIE'] != 0]
T10 = T10[93:]
path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\macro\UNRATE.xls"
UNRATE = process(path, "FRED Graph", 9, 1)
UNRATE = UNRATE[850:]
plt.figure(figsize=(18, 10))
plot_dataframe(CPI, "Consumer Price Index","MONTH","CPI")
plt.figure(figsize=(18, 10))
plot_dataframe(T10, "Ten Year Breakeven Inflation Point","MONTH","inflation point")
plt.figure(figsize=(18, 10))
plot_dataframe(UNRATE, "Unemployment Rate","MONTH","unemployment rate")

Ecomonic Indicators

• Consumer Price Index for All Urban Consumers A measurment of the changes in average prices paid by consumbers for goods and services. These goods/services include food, clothing, rent, healthcare and transportation. It is a key indicator of inflation; Rising CPI indicates ricing prices.
• 10-Year Breakeven Inflation Rate, Percent, Daily, Not Seasonally Adjusted Indicates the expected annual inflation over the next ten years, implied by the yield difference between 10 year treasury bonds and 10 year trasury inflation proteced securities. A rising breakeven rate suggest that investors expect higher inflation.
• Unemployment Rate, Percent, Monthly, Seasonally Adjusted Measures the percentage of the labor force that is currently unemployed and actively looking for a job. A high unemployment rate may indicate economic distress, while a low rate suggests a healthy job market.

Overall, these indicators are essential tools for understanding and managing economic conditions, making informed investment decisions, and formulating appropriate economic policies.

US TREASURY SECURITIES

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Treasury-Securities-Statistics-SIFMA.xlsx"
#Treasury Securities
#Clean/Extract the data 
us_treasury = process(path, "Trading Volume", 7, 5)
us_treasury_trading_volume_monthly = us_treasury[25:]
plt.figure(figsize=(18, 10))
#Plot the time-series data for SIFMA for the time period that each series is available
plot_dataframe(us_treasury_trading_volume_monthly,"Monthly Trading Volume","MONTH","Amount in Billions")

#Coupon Securities
us_treasury = process(path, "Trading Volume", 7, 14)
us_treasury_trading_coupon = us_treasury.iloc[25:,6:]
plt.figure(figsize=(18, 10))
#Plot the time-series data for SIFMA for the time period that each series is available
plot_dataframe(us_treasury_trading_coupon,"Monthly Trading Volume of Coupon Securities","MONTH","Amount in Billions")


#TIPS
us_treasury = process(path, "Trading Volume", 7, 18)
us_treasury_trading_tips = us_treasury.iloc[25:,15:]
plt.figure(figsize=(18, 10))
#Plot the time-series data for SIFMA for the time period that each series is available
plot_dataframe(us_treasury_trading_tips,"Monthly Trading Volume of TIPS","MONTH","Amount in Billions")

descriptive_stats(us_treasury_trading_volume_monthly)
descriptive_stats(us_treasury_trading_coupon)
descriptive_stats(us_treasury_trading_tips)

treasury = pd.concat([us_treasury_trading_volume_monthly, us_treasury_trading_coupon, us_treasury_trading_tips])
sns.heatmap(treasury.corr())

Variable:  Treasury Bills
------------------  ------------------  ------------------  ------------------  ------  ------------------  -----------------
mean                std                 min                 25%                 50%     75%                 max
148.34719510085642  18.633445190527013  124.97619047619048  127.01428571428572  152.94  161.23333333333332  173.1478260869565
------------------  ------------------  ------------------  ------------------  ------  ------------------  -----------------


Variable:  Treasury Inflation Index Securities (TIPS)
------------------  ------------------  ------------------  ------------------  ------------------  ------  ------
mean                std                 min                 25%                 50%                 75%     max
15.351622458395914  3.4205463264144242  11.157142857142858  13.513636363636364  14.585714285714289  17.685  21.725
------------------  ------------------  ------------------  ------------------  ------------------  ------  ------


Variable:  Floating Rate Notes (FRNs)
------------------  ------------------  ---  ------------------  ---  ------------------  -----------------
mean                std                 min  25%                 50%  75%                 max
3.7982232755802547  2.2558062068584066  1.3  2.1904761904761907  3.4  4.4526315789473685  9.294736842105262
------------------  ------------------  ---  ------------------  ---  ------------------  -----------------


Variable:  Coupon Securities
-----------------  -----------------  ----------------  ------------------  -----------------  -----------------  -----------------
mean               std                min               25%                 50%                75%                max
542.0776442893491  80.55315772002777  363.852380952381  497.89047619047614  538.3210526315789  588.2454545454545  676.8347826086957
-----------------  -----------------  ----------------  ------------------  -----------------  -----------------  -----------------


Variable:  Total
-----------------  -----------------  -----------------  -----------------  -----------------  -----------------  -----------------
mean               std                min                25%                50%                75%                max
709.5761435399078  97.70571955336267  502.9000000000001  665.9526315789474  695.5649999999999  774.2318181818181  875.2130434782609
-----------------  -----------------  -----------------  -----------------  -----------------  -----------------  -----------------


Variable:  Coupon Securities Due =< 2Y
------------------  ------------------  -----------------  -----------------  ------------------  ------------------  ------------------
mean                std                 min                25%                50%                 75%                 max
104.37153194631456  12.895127796852494  72.85238095238095  100.0578947368421  105.26190476190476  109.99565217391303  127.49565217391304
------------------  ------------------  -----------------  -----------------  ------------------  ------------------  ------------------


Variable:  Coupon Securities Due > 2Y but =< 3Y
-----------------  ------------------  -----------------  -----------------  ------  -----------------  ------
mean               std                 min                25%                50%     75%                max
68.17931027782286  10.497545980932532  51.13333333333333  62.76190476190476  66.485  71.28947368421052  89.465
-----------------  ------------------  -----------------  -----------------  ------  -----------------  ------


Variable:  Coupon Securities Due > 3Y but =<5Y
------------------  -----------------  -----------------  ------------------  ------------------  ------------------  ------------------
mean                std                min                25%                 50%                 75%                 max
148.70690867103912  26.64045651136267  95.68095238095238  139.22105263157897  145.37142857142857  162.13333333333333  202.29130434782607
------------------  -----------------  -----------------  ------------------  ------------------  ------------------  ------------------


Variable:  Coupon Securities Due > 5Y but =< 7Y
------------------  ----------------  ------------------  -----------------  ------------------  ------------------  -----------------
mean                std               min                 25%                50%                 75%                 max
47.165837408205824  8.28019632611027  29.114285714285714  43.30952380952381  46.573684210526324  54.304545454545455  57.51304347826087
------------------  ----------------  ------------------  -----------------  ------------------  ------------------  -----------------


Variable:  Coupon Securities Due > 7Y but =< 10Y
-----------------  ------------------  -----------------  -----------------  ------------------  ------------------  ------------------
mean               std                 min                25%                50%                 75%                 max
107.5026973446081  16.551918587372395  69.07619047619048  98.86190476190475  106.18947368421054  118.41363636363636  129.66086956521738
-----------------  ------------------  -----------------  -----------------  ------------------  ------------------  ------------------


Variable:  Coupon Securities Due > 10Y
----  ---  ---  ---  ---  ---  ---
mean  std  min  25%  50%  75%  max
0.0   0.0  0    0.0  0.0  0.0  0
----  ---  ---  ---  ---  ---  ---


Variable:  Coupon Securities Due > 10Y but =< 20Y
------------------  -----------------  ------------------  ------------------  ------------------  -----  ------------------
mean                std                min                 25%                 50%                 75%    max
16.724578255459264  2.867781438830915  11.478947368421052  15.561904761904762  17.456521739130434  18.25  21.704347826086956
------------------  -----------------  ------------------  ------------------  ------------------  -----  ------------------


Variable:  Coupon Securities Due > 20Y
-----------------  -----------------  -----------------  -----------------  -----------------  -----------------  -----------------
mean               std                min                25%                50%                75%                max
49.42913251889225  9.416315628844274  34.10952380952381  41.18095238095238  51.95652173913044  57.18421052631579  62.08695652173913
-----------------  -----------------  -----------------  -----------------  -----------------  -----------------  -----------------


Variable:  TIPS Due =< 5Y
-----------------  ------------------  -----------------  -----------------  -----------------  -----------------  ------------------
mean               std                 min                25%                50%                75%                max
9.244168219271195  2.2399402288963586  5.714285714285714  8.290909090909091  8.747826086956522  10.30952380952381  13.238095238095237
-----------------  ------------------  -----------------  -----------------  -----------------  -----------------  ------------------


Variable:  TIPS Due in > 5Y but =< 10Y
----------------  -----------------  ------------------  ------------------  -----------------  -----------------  -----
mean              std                min                 25%                 50%                75%                max
4.79829607844482  1.408442381969265  3.1523809523809527  3.9368421052631586  4.371428571428572  5.371428571428571  8.405
----------------  -----------------  ------------------  ------------------  -----------------  -----------------  -----


Variable:  TIPS Due > 10Y
------------------  -------------------  ------------------  ----  ------------------  -----------------  ------------------
mean                std                  min                 25%   50%                 75%                max
1.3115144999927608  0.33946227846915167  0.9210526315789473  1.06  1.2956521739130435  1.434782608695652  2.1428571428571423
------------------  -------------------  ------------------  ----  ------------------  -----------------  ------------------

<Axes: xlabel='0', ylabel='0'>

• Monthly trading volume dropped at the beginning of the year, but rose back to a normal level.
• treasury trading volume less correlated than coupon securities trading volume.

US GCF REPO INDEX

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Repo-Statistics-SIFMA.xlsx"

#US REPO INDEX 
us_repo_monthly_trading_volume = process(path, "Primary Dealer", 7, 3)
us_repo_monthly_trading_volume = us_repo_monthly_trading_volume[25:]
plt.figure(figsize=(18, 10))
#Plot the time-series data for SIFMA for the time period that each series is available
plot_dataframe(us_repo_monthly_trading_volume,"Repo Monthly Trading Volume","MONTH","Amount in Billions")

#Coupon Securities
us_repo_reverse = process(path, "Primary Dealer", 7, 7)
us_repo_reverse = us_repo_reverse.iloc[25:,4:]
plt.figure(figsize=(18, 10))
#Plot the time-series data for SIFMA for the time period that each series is available
plot_dataframe(us_repo_reverse,"Reverse Repo","MONTH","Amount in Billions")

repo = process(path, "Primary Dealer", 7,17)
repo_monthly_companies = repo.iloc[25:,10:]
plt.figure(figsize=(18, 10))
plot_dataframe(repo_monthly_companies,"COMPANIES Repo","MONTH","Amount in Billions")

repo_mbs = process(path, "Primary Dealer", 7,29)
repo_monthly_reverse = repo_mbs.iloc[25:,22:]
# repo_monthly
plt.figure(figsize=(18, 10))
plot_dataframe(repo_monthly_reverse,"Reverse Repo","MONTH","Amount in Billions")

repo = process(path, "Primary Dealer", 7,21)
repo_mbs = repo.iloc[25:,19:]
# repo_monthly
plt.figure(figsize=(18, 10))
plot_dataframe(repo_mbs,"Repo-MBS","MONTH","Amount in Billions")



reverse_repo = process(path, "Primary Dealer", 7,33)
rev_repo_mbs = reverse_repo.iloc[25:,31:]
# rev_repo_monthly
plt.figure(figsize=(18, 10))
plot_dataframe(rev_repo_mbs,"Reverse MBS Repo","MONTH","Amount in Bilions")

repo = pd.concat([us_repo_monthly_trading_volume, us_repo_reverse, repo_monthly_companies, repo_monthly_reverse, repo_mbs, rev_repo_mbs], axis=1)
sns.heatmap(repo.corr())
descriptive_stats(us_repo_monthly_trading_volume), descriptive_stats(us_repo_reverse)

Variable:  Overnight
------------------  -----------------  ----------  ------------------  ----------  --------  --------
mean                std                min         25%                 50%         75%       max
2063.8129230769227  190.0971200696871  1808.82775  1906.5153999999998  2058.99575  2235.818  2329.245
------------------  -----------------  ----------  ------------------  ----------  --------  --------


Variable:  Term
-----------------  -----------------  ---------  --------  --------  --------  -----------------
mean               std                min        25%       50%       75%       max
627.1311076923078  28.32695778666388  575.55475  608.7875  627.7986  633.8608  675.7515000000001
-----------------  -----------------  ---------  --------  --------  --------  -----------------


Variable:  Total
------------------  ------------------  --------  ------------------  ------------------  ------------------  ------------------
mean                std                 min       25%                 50%                 75%                 max
2690.9440307692307  186.46493600269233  2429.474  2537.3577999999998  2634.5505000000003  2838.3762500000003  2987.5815000000002
------------------  ------------------  --------  ------------------  ------------------  ------------------  ------------------


Variable:  Overnight
-----------------  -----------------  ------------------  ---------  ----------  ---------  ---------
mean               std                min                 25%        50%         75%        max
1210.190976923077  86.67809101223484  1053.7859999999998  1128.7324  1225.76525  1273.2182  1341.8086
-----------------  -----------------  ------------------  ---------  ----------  ---------  ---------


Variable:  Term
-----------------  -----------------  -----------------  --------  ---------  -------  --------
mean               std                min                25%       50%        75%      max
907.9490346153846  53.17832181419725  835.3655000000001  872.3422  901.84175  941.415  1035.566
-----------------  -----------------  -----------------  --------  ---------  -------  --------


Variable:  Total
------------------  -----------------  ---------  ---------  ------------------  ----------  ------------------
mean                std                min        25%        50%                 75%         max
2118.1400115384613  121.2866071224756  1938.7205  2016.5834  2112.5232499999997  2201.28325  2322.1665000000003
------------------  -----------------  ---------  ---------  ------------------  ----------  ------------------

(None, None)

• Overall repo trading volume has increased overtime.
• Comparing to the macro economic factors, there is little to no correlation, they do not seem to be related.

US EQUITY ISSUANCE AND TRADING VOLUMES

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Equities-and-Related-Statistics-SIFMA.xlsx"

EOP = process(path, "Indexes & Volatility", 7,5)
EOP_monthly = EOP.iloc[25:]

plt.figure(figsize=(18, 10))
plot_dataframe(EOP_monthly,"END OF PERIOD INDEXES","MONTH","Amount")

eop_vix = process(path, "Indexes & Volatility", 7,6)
eop_vix = eop_vix.iloc[25:, 5:]
# eop_vix
plt.figure(figsize=(18, 10))
plot_dataframe(eop_vix,"END OF PERIOD VIX","MONTH","Amount")

ave = process(path, "Indexes & Volatility", 7,12)
ave_monthly = ave.iloc[25:,7:]
ave_monthly
plt.figure(figsize=(14, 8))
plot_dataframe(ave_monthly,"END OF PERIOD INDEXES","MONTH","Amount")


ave_vix = process(path, "Indexes & Volatility", 7,13)
ave_vix = ave_vix.iloc[25:, 12:]
# eop_vix
plt.figure(figsize=(14, 8))
plot_dataframe(ave_vix,"AVERAGE VIX","MONTH","Amount")

tape_volume = process(path, "ADV #", 7,3)
tape_volume_monthly = tape_volume.iloc[25:]
# tape_volume_monthly
plt.figure(figsize=(18, 10))
plot_dataframe(tape_volume_monthly,"Average Daily Trading Share - TAPE","MONTH","Amount")

equities = pd.concat([EOP_monthly, eop_vix, ave_monthly, ave_vix, tape_volume_monthly])
descriptive_stats(equities)
sns.heatmap(equities.corr())

Variable:  DJIA
-----------------  ------------------  --------  --------  --------  ------------------  --------
mean               std                 min       25%       50%       75%                 max
33228.72884615385  1509.1045909951422  28725.51  32776.78  33450.11  34095.130000000005  35559.53
-----------------  ------------------  --------  --------  --------  ------------------  --------


Variable:  S&P 500
-----------------  ------------------  -------  ---------  --------  ---------  -------
mean               std                 min      25%        50%       75%        max
4097.574230769231  255.38965354411798  3585.62  3926.8675  4079.895  4177.2425  4588.96
-----------------  ------------------  -------  ---------  --------  ---------  -------


Variable:  Nasdaq
------------------  ------------------  --------  ---------  ---------  ------------------  --------
mean                std                 min       25%        50%        75%                 max
12103.345384615388  1181.6238199705717  10466.48  11111.205  11800.635  12844.032500000001  14346.02
------------------  ------------------  --------  ---------  ---------  ------------------  --------


Variable:  Russell 2000
------------------  -----------------  --------  ------------------  --------  ----------  --------
mean                std                min       25%                 50%       75%         max
1840.6481923076924  79.50916866124017  1664.716  1776.7115000000001  1845.489  1899.00475  2003.177
------------------  -----------------  --------  ------------------  --------  ----------  --------


Variable:  NYSE
------------------  -----------------  --------  ----------  ------------------  ----------  --------
mean                std                min       25%         50%                 75%         max
15342.600000000002  662.7680159030007  13472.18  15090.3125  15413.755000000001  15815.3825  16427.29
------------------  -----------------  --------  ----------  ------------------  ----------  --------


Variable:  VIX
------------------  -----------------  -----  ------  -----  ------  -----
mean                std                min    25%     50%    75%     max
20.093076923076918  5.091771563200952  13.57  15.815  20.12  21.975  31.62
------------------  -----------------  -----  ------  -----  ------  -----


Variable:  Tape A
-----------------  ------------------  ------------------  ------------------  ----------  -----------------  -----------------
mean               std                 min                 25%                 50%         75%                max
4167.115940710044  325.36901620447725  3726.4646992105263  3977.7235415500004  4108.88668  4359.974996761905  4882.348975173913
-----------------  ------------------  ------------------  ------------------  ----------  -----------------  -----------------


Variable:  Tape B
------------------  ------------------  -------------  ------------------  ------------------  ------------------  ------------------
mean                std                 min            25%                 50%                 75%                 max
1947.0017884776157  228.84848443891593  1640.74999445  1763.1052220526317  1945.6924946842105  2043.6102381904761  2349.4100969565216
------------------  ------------------  -------------  ------------------  ------------------  ------------------  ------------------


Variable:  Tape C
-----------------  -----------------  -----------------  -----------------  -----------------  -----------------  ------------
mean               std                min                25%                50%                75%                max
4957.548582008002  246.6139603444129  4689.824014857142  4771.861056142858  4837.869992142858  5245.417413428571  5370.6602412
-----------------  -----------------  -----------------  -----------------  -----------------  -----------------  ------------

<Axes: xlabel='0', ylabel='0'>

Do you see any patterns in the time-series? Is there any seasonality?

• Intrestingly, there is an increase in trading volume in april 2023. Aside from that, everything looks pretty consistent, except for the total market.
• There are no patterns comparing to the macro-economic indicators.

US FIXED INCOME SECURITIES

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Fixed-Income-Securities-Statistics-SIFMA.xlsx"
trading_vol_fixed = process(path, "Trading Volume", 6, 8)
trading_vol_fixed_monthly = trading_vol_fixed.iloc[25:]

plt.figure(figsize=(18, 10))
plot_dataframe(trading_vol_fixed_monthly,"Fixed Income Trading Volumne","MONTH","Amount")

descriptive_stats(trading_vol_fixed_monthly)
sns.heatmap(trading_vol_fixed_monthly.corr())

Variable:  UST
-----------------  ------------------  -----------------  -----------------  -----------------  -----------------  -----------------
mean               std                 min                25%                50%                75%                max
695.5023977205099  104.21594651375749  502.9000000000001  639.4190476190475  691.2565217391304  774.2318181818181  875.2130434782609
-----------------  ------------------  -----------------  -----------------  -----------------  -----------------  -----------------


Variable:  Agency MBS
-----------------  ------------------  ------------------  ------------------  ------------------  -----------------  ------------
mean               std                 min                 25%                 50%                 75%                max
237.3203247236577  16.449672691474973  198.10003704761905  230.66942711904755  239.54840403636362  245.3025820263158  263.84720799
-----------------  ------------------  ------------------  ------------------  ------------------  -----------------  ------------


Variable:  Non-Agency MBS
-----------------  ------------------  ------------------  ------------------  ------------------  -----------------  ------------------
mean               std                 min                 25%                 50%                 75%                max
1.373116034940536  0.3790460899047805  1.0378969913043476  1.1671315136363638  1.2494920142857142  1.483317978947368  2.4139013649999996
-----------------  ------------------  ------------------  ------------------  ------------------  -----------------  ------------------


Variable:  Corporates
------------------  ----------------  ------------------  ------------------  -----------------  -------  -----------------
mean                std               min                 25%                 50%                75%      max
38.976202348261964  4.85772928841071  30.786285714285718  36.651700000000005  38.51604761904762  39.9385  47.91905263157895
------------------  ----------------  ------------------  ------------------  -----------------  -------  -----------------


Variable:  Munis
------------------  ------------------  ------  ------  ------  ------  ------
mean                std                 min     25%     50%     75%     max
13.461000000000002  1.5718287650589253  11.334  12.054  13.133  15.032  15.882
------------------  ------------------  ------  ------  ------  ------  ------


Variable:  Agency
-----------------  ------------------  -------  -----------------  ------------------  ------------------  -----------------
mean               std                 min      25%                50%                 75%                 max
3.167966855760562  0.8063862199063073  2.25865  2.490333333333333  3.1824761904761907  3.4757894736842103  4.550545454545454
-----------------  ------------------  -------  -----------------  ------------------  ------------------  -----------------


Variable:  ABS
------------------  -------------------  ------------------  ------------------  ------------------  ------------------  -----------------
mean                std                  min                 25%                 50%                 75%                 max
1.6148484321709755  0.31924725008888366  1.0670910900000001  1.4531820315789472  1.6201566099999998  1.8327452052631577  2.094405247619047
------------------  -------------------  ------------------  ------------------  ------------------  ------------------  -----------------


Variable:  Total
-----------------  ------------------  -----------------  -----------------  -----------------  -----------------  ------------------
mean               std                 min                25%                50%                75%                max
991.4158561153016  121.04320011510104  751.5207341333333  937.8724787571429  996.1191188947369  1071.877926059091  1187.9895073110674
-----------------  ------------------  -----------------  -----------------  -----------------  -----------------  ------------------

<Axes: xlabel='0', ylabel='0'>

Do you see any patterns in the time-series? Is there any seasonality?

• Fixed income trading volume is consistent, aside from the UST.
• Unemployment rate seems somewhat similar to the pattern of US Treasury in fixed income.

Corporate Bonds

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Corporate-Bonds-Statistics-SIFMA.xlsx"

trading_vol = process(path, "Trading Volume", 7, 3)
trading_vol_public = trading_vol.iloc[25:]

plt.figure(figsize=(18, 10))
plot_dataframe(trading_vol_public,"Publicly Traded","YEAR","Amount in Billions")

trading_vol = process(path, "Trading Volume", 7, 7)
trading_vol_14A = trading_vol.iloc[25:, 4:]

plt.figure(figsize=(18, 10))
plot_dataframe(trading_vol_14A,"14A","YEAR","Amount in Billions")


trading_vol = process(path, "Trading Volume", 7, 11)
trading_vol_NonConvert = trading_vol.iloc[25:, 8:]

plt.figure(figsize=(18, 10))
plot_dataframe(trading_vol_NonConvert,"Total Nonconvertible","YEAR","Amount in Billions")

trading_vol = process(path, "Trading Volume", 7, 13)
trading_vol_convert = trading_vol.iloc[25:, 12:]

plt.figure(figsize=(18, 10))
plot_dataframe(trading_vol_convert,"Total Convertible","YEAR","Amount in Billions")

bonds = pd.concat([trading_vol_public, trading_vol_14A, trading_vol_NonConvert, trading_vol_convert], axis =1)
descriptive_stats(trading_vol_public)
sns.heatmap(bonds.corr())

Variable:  Investment Grade
------------------  ------------------  ------  -----------------  -----------------  -----------------  ------------------
mean                std                 min     25%                50%                75%                max
23.705957477533907  2.7674781935462427  19.262  22.73278947368421  23.23633333333333  24.51440909090909  28.367736842105263
------------------  ------------------  ------  -----------------  -----------------  -----------------  ------------------


Variable:  High Yield
----------------  ------------------  -----------------  -----------------  -----------------  -----------------  -----------------
mean              std                 min                25%                50%                75%                max
5.70585796979527  1.0654578739740075  3.872047619047619  5.197789473684211  5.486826086956522  6.307318181818182  7.950736842105264
----------------  ------------------  -----------------  -----------------  -----------------  -----------------  -----------------


Variable:  Total
------------------  -----------------  -----------------  -----------------  ------------------  ------------------  ------------------
mean                std                min                25%                50%                 75%                 max
29.411815447329175  3.736194562165449  23.13404761904762  27.93057894736842  28.797333333333334  30.821727272727273  36.318473684210524
------------------  -----------------  -----------------  -----------------  ------------------  ------------------  ------------------

<Axes: xlabel='0', ylabel='0'>

• Here we see spikes in trading volume at the begninning of the year, this returns to normal halfway through the year. Investors purchase at the beginning of the year to secure a known and predictable income stream for the entire year. This is a way to capture a full years worht of coupon payments.
• This does not appear to be related to the macro economic factors I selected.

US ASSET BACKED SECURITIES

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Asset-Backed-Securities-Statistics-SIFMA.xlsx"

abs_trading_volume = process(path, "ABS Trading Volume - $", 6,6)
abs_trading_volume = abs_trading_volume.iloc[25:,]
plt.figure(figsize=(18, 10))
plot_dataframe(abs_trading_volume.head(13),"VOLUME","YEAR","Amount in Millions")

descriptive_stats(abs_trading_volume)
sns.heatmap(abs_trading_volume.corr())

Variable:  ABS
-----------------  ----------------  --------  --------  -----------------  -----------  ---------
mean               std               min       25%       50%                75%          max
918.9811295923331  272.719812764323  506.3517  684.1238  933.5327478260868  1056.964025  1519.5542
-----------------  ----------------  --------  --------  -----------------  -----------  ---------


Variable:  CDO
-----------------  -----------------  ------------------  ----------  -----------------  ------------------  ---------
mean               std                min                 25%         50%                75%                 max
830.4921834242049  310.7666296459455  295.78770000000003  625.155375  790.0063842105262  1003.3599321428572  1753.6299
-----------------  -----------------  ------------------  ----------  -----------------  ------------------  ---------


Variable:  Other
-------------------  -------------------  ---  ---  ---  ---  ------------------
mean                 std                  min  25%  50%  75%  max
0.03373015873015873  0.20238095238095236  0    0.0  0.0  0.0  1.2142857142857142
-------------------  -------------------  ---  ---  ---  ---  ------------------


Variable:  Total
------------------  -----------------  --------  -----------------  ------------------  -----------  --------
mean                std                min       25%                50%                 75%          max
1749.5070431752672  485.1763945558023  882.8313  1368.765193478261  1790.0918749482403  2088.425425  2657.556
------------------  -----------------  --------  -----------------  ------------------  -----------  --------


Variable:   IG 
------------------  ----------------  -----------------  ------------------  ------------------  -----------  ---------
mean                std               min                25%                 50%                 75%          max
1404.6772000002472  398.957099337268  783.7017000000001  1110.7044761904763  1355.3064050000003  1632.058125  2308.5338
------------------  ----------------  -----------------  ------------------  ------------------  -----------  ---------


Variable:   HY 
-----------------  ------------------  -------  ----------  -----------------  ------------------  -----------------
mean               std                 min      25%         50%                75%                 max
344.8298431750202  158.28425315617508  95.2312  226.962825  317.8383347826086  422.15451428571436  716.9507714285713
-----------------  ------------------  -------  ----------  -----------------  ------------------  -----------------

<Axes: xlabel='0', ylabel='0'>

Do you see any patterns in the time-series? Is there any seasonality?

• Asset Backed securities trading volume dips at the end of the year and picks back up at the beginning of the year.
• No patterns seen with the macro economic indicators.

US AGENCY DEBT

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Agency-Debt-Statistics-SIFMA.xlsx"
agency_trading_volume = process(path, "Trading Volume", 6, 5)
agency_trading_volume = agency_trading_volume.iloc[25:,]
plt.figure(figsize=(18, 10))
plot_dataframe(agency_trading_volume,"Trading Volume","YEAR","Amount in Billions")

descriptive_stats(agency_trading_volume)
sns.heatmap(agency_trading_volume.corr())

Variable:  Fannie Mae
-------------------  --------------------  -------------------  -------------------  -------------------  -------------------  ------------------
mean                 std                   min                  25%                  50%                  75%                  max
0.21846673787692097  0.046190416951699224  0.12954545454545452  0.20410526315789473  0.22752380952380952  0.23613636363636362  0.2985217391304348
-------------------  --------------------  -------------------  -------------------  -------------------  -------------------  ------------------


Variable:  FHLB
-----------------  ------------------  ------------------  ------------------  ------  -------  ------------------
mean               std                 min                 25%                 50%     75%      max
1.467288512669634  0.7390423017156353  0.6292857142857143  1.0136190476190476  1.1036  1.63405  2.9731363636363635
-----------------  ------------------  ------------------  ------------------  ------  -------  ------------------


Variable:  Freddie Mac
------------------  ------------------  -------------------  -------  -----------------  -------  -------
mean                std                 min                  25%      50%                75%      max
1.0181430548856178  0.4461218131509146  0.34942105263157897  0.81265  0.931952380952381  1.25855  2.09485
------------------  ------------------  -------------------  -------  -----------------  -------  -------


Variable:  Other
-------------------  ------------------  -------  -----  -------------------  ------------------  ------------------
mean                 std                 min      25%    50%                  75%                 max
0.46406855032839006  0.3530731062921647  0.13135  0.165  0.20661904761904762  0.7974090909090908  0.9963809523809524
-------------------  ------------------  -------  -----  -------------------  ------------------  ------------------


Variable:  Total
-----------------  ------------------  -------  -----------------  ------------------  ------------------  -----------------
mean               std                 min      25%                50%                 75%                 max
3.167966855760562  0.8063862199063073  2.25865  2.490333333333333  3.1824761904761907  3.4757894736842103  4.550545454545454
-----------------  ------------------  -------  -----------------  ------------------  ------------------  -----------------

<Axes: xlabel='0', ylabel='0'>

• US Agency debt for FHLB, Freddie Mac, and Other all increase after 2023.
• This sort of follows what the 10Y breakeven inflection point. Consumer price index increases as well.

Cross Market Correlation of Securities

securities = pd.concat([agency_trading_volume, us_treasury_trading_volume_monthly, trading_vol_fixed_monthly], axis = 1)
sns.heatmap(securities.corr())

<Axes: xlabel='0', ylabel='0'>

• Here we can see there is a lot of correlation going on. Fannie Mae volume and Munis are the most negatively correlated with the others.

ISSUANCE STATISTICS

Treasury Issuance, OUtstanding, and Interest Rates

#US Marketable Treasury Issuance, Outstanding, and Interest Rates
path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Treasury-Securities-Statistics-SIFMA.xlsx"

#Total Issuance
treasury_issuance = process(path, "Issuance Net", 7, 15)
treasury_issuance = treasury_issuance.iloc[25:,12:]
plt.figure(figsize=(18, 10))
plot_dataframe(treasury_issuance,"Treasury Issuance Total","Month","Amount in Billions")


treasury_outstanding = process(path, "Outstanding", 6, 6)
treasury_outstanding = treasury_outstanding.iloc[25:,]
plt.figure(figsize=(18, 10))
plot_dataframe(treasury_outstanding,"Treasury Outsanding","Month","Amount in Billions")

treasury_interest_rates = process(path, "Yield Curve Rates", 7, 3)
treasury_interest_rates = treasury_interest_rates.iloc[25:,]
plt.figure(figsize=(18, 10))
plot_dataframe(treasury_interest_rates,"Yiled Curve Rates","Month","Averages for the period")

descriptive_stats(treasury_issuance)
descriptive_stats(treasury_outstanding)
descriptive_stats(treasury_interest_rates)

treasury = pd.concat([treasury_issuance, treasury_outstanding, treasury_interest_rates], axis = 1)
sns.heatmap(treasury.corr())

Variable:  Gross Issues
------------------  -----------------  ------------------  --------  --------  -------  --------
mean                std                min                 25%       50%       75%      max
1637.2791538461538  311.9681652804888  1202.3000000000002  1342.698  1628.992  1771.21  2316.938
------------------  -----------------  ------------------  --------  --------  -------  --------


Variable:  Gross Retirement
------------------  ------------------  --------  --------  ------------------  --------  --------
mean                std                 min       25%       50%                 75%       max
1480.4852307692306  215.16502572876234  1267.659  1324.419  1428.1149999999998  1500.136  1983.702
------------------  ------------------  --------  --------  ------------------  --------  --------


Variable:  Net Cash Raised
------------------  ------------------  ------------------  -----------------  ------------------  -----------------  -----------------
mean                std                 min                 25%                50%                 75%                max
156.79392307692308  175.14458361139876  -106.4219999999999  33.20700000000008  160.50500000000017  246.3559999999998  549.9660000000001
------------------  ------------------  ------------------  -----------------  ------------------  -----------------  -----------------


Variable:  Bills
------------------  -----------------  ------------  ------------  ------------------  --------------  ------------
mean                std                min           25%           50%                 75%             max
4041.4033584799995  341.2441232588406  3665.9784493  3843.6472255  3968.0332946500002  4066.069294925  4770.5145957
------------------  -----------------  ------------  ------------  ------------------  --------------  ------------


Variable:  Notes
--------------  -----------------  ------------------  --------------  --------------  --------------  ------------
mean            std                min                 25%             50%             75%             max
13737.48789827  17.79871492113009  13717.937353700001  13725.60748415  13733.15774545  13748.40087005  13774.295519
--------------  -----------------  ------------------  --------------  --------------  --------------  ------------


Variable:  Bonds
--------------  ------------------  ------------------  -------------  --------------  ------------  ------------------
mean            std                 min                 25%            50%             75%           max
4049.953438315  100.10694514869078  3904.3402751500003  3970.37063155  4048.693886475  4126.0839034  4200.3508120999995
--------------  ------------------  ------------------  -------------  --------------  ------------  ------------------


Variable:  TIPS
-----------------  ---------------  ----------------  ------------------  -----------------  ----------------  ------------------
mean               std              min               25%                 50%                75%               max
1892.367116866934  22.007550154117  1860.88587893899  1877.1783229244425  1891.836154422595  1905.43015793628  1933.6292502419699
-----------------  ---------------  ----------------  ------------------  -----------------  ----------------  ------------------


Variable:  FRN
-----------------  ------------------  -----------------  -------------  -----------  -------------  -----------------
mean               std                 min                25%            50%          75%            max
580.3569862900001  26.079755577900407  527.5666633999999  566.575976425  582.8840241  599.858710725  617.2041916999999
-----------------  ------------------  -----------------  -------------  -----------  -------------  -----------------


Variable:  Total
-----------------  ------------------  ------------------  ------------------  ------------------  -----------------  -----------------
mean               std                 min                 25%                 50%                 75%                max
24301.56879822193  427.09623236442525  23738.638483888986  23992.180545090563  24279.333289968785  24363.41054446128  25132.50363145857
-----------------  ------------------  ------------------  ------------------  ------------------  -----------------  -----------------


Variable:  3-Month Bills
-----------------  ------------------  -----------------  ------------------  -----------------  -----------------  -----------------
mean               std                 min                25%                 50%                75%                max
4.590488927548424  0.8803739098520452  2.721739130434783  4.3229999999999995  4.787894736842103  5.308181818181819  5.559130434782609
-----------------  ------------------  -----------------  ------------------  -----------------  -----------------  -----------------


Variable:  10-Year Notes
-----------------  -------------------  ------------------  ------------------  ------------------  ------------------  -----------------
mean               std                  min                 25%                 50%                 75%                 max
3.669077496492829  0.31013008786691804  2.8978260869565213  3.5315000000000003  3.6630434782608696  3.8909999999999996  4.167826086956523
-----------------  -------------------  ------------------  ------------------  ------------------  ------------------  -----------------


Variable:  Spread
-------------------  ------------------  -------------------  -------------------  -------------------  --------------------  -------------------
mean                 std                 min                  25%                  50%                  75%                   max
-0.9214114310555956  0.7379602407849437  -1.7345454545454548  -1.5889999999999995  -1.1559999999999997  -0.43199999999999994  0.29714285714285715
-------------------  ------------------  -------------------  -------------------  -------------------  --------------------  -------------------

<Axes: xlabel='0', ylabel='0'>

• Gross treasury and gross issues follow the same path. These also follow the same path that the 10Y inflation point follows.
• The spread of 3 month bills and 10 year notes has decreased since last year.
• There is negative correlation (an inverse relationship) between spread, Notes, and FRN with the other factors.

MBS Issuance and Outsanding

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Mortgage-Backed-Securities-Statistics-SIFMA.xlsx"
#Issuance
MBS_agency = process(path, "MBS Issuance", 17, 2)
MBS_agency = MBS_agency.iloc[49:,]

MBS_non_agency = process(path, "MBS Issuance", 17, 5)
MBS_non_agency = MBS_non_agency.iloc[49:, 3:]

MBS_MRS = process(path, "MBS Issuance", 17, 9)
MBS_MRS = MBS_MRS.iloc[49:, 6:]

MBS_issuance = pd.concat([MBS_agency, MBS_non_agency, MBS_MRS], axis=1)

plt.figure(figsize=(18, 10))
plot_dataframe(MBS_issuance,"MBS Issuance","Month","Amount in Billions")

#Outstanding
MBS_agency_OS = process(path, "MBS Outstanding", 16, 2)
MBS_agency_OS = MBS_agency_OS.iloc[20:,]

MBS_non_agency_OS = process(path, "MBS Outstanding", 16, 5)
MBS_non_agency_OS = MBS_non_agency_OS.iloc[20:, 3:]

MBS_total = process(path, "MBS Outstanding", 16, 9)
MBS_total = MBS_total.iloc[20:, 6:]


MBS_outstanding = pd.concat([MBS_agency_OS, MBS_non_agency_OS, MBS_total], axis =1 )
plt.figure(figsize=(18, 10))
plot_dataframe(MBS_outstanding,"MBS Outstanding","Month","Amount in Billions")

descriptive_stats(MBS_issuance)
descriptive_stats(MBS_outstanding)

MBS = pd.concat([MBS_issuance, MBS_outstanding], axis = 1)
sns.heatmap(MBS.corr())

Variable:  MBS
-----------------  -----------------  ------------------  ------------  --------------  ---------------  ------------------
mean               std                min                 25%           50%             75%              max
275.0658559833637  75.19468413680605  150.63135977500002  195.99918314  287.3425302165  345.25162938375  384.69112549100004
-----------------  -----------------  ------------------  ------------  --------------  ---------------  ------------------


Variable:  CMO
-----------------  -----------------  ------------  ------------------  ------------  --------------  ------------------
mean               std                min           25%                 50%           75%             max
37.11012680722729  8.741986039187386  24.615618023  30.794656976500004  37.470843284  41.83597430975  60.132218357000006
-----------------  -----------------  ------------  ------------------  ------------  --------------  ------------------


Variable:  CMBS
-----------------  -----------------  --------  --------  -----------------  ---------  ---------
mean               std                min       25%       50%                75%        max
6.019945772727271  4.119433128183409  0.306414  3.476985  4.303221499999999  8.3621195  16.085979
-----------------  -----------------  --------  --------  -----------------  ---------  ---------


Variable:  RMBS
------------------  ------------------  --------  -----------------  ---------  ------------------  ---------
mean                std                 min       25%                50%        75%                 max
13.740011954545459  13.302883004055682  2.829632  4.995540500000001  9.6678605  15.882205500000001  57.913502
------------------  ------------------  --------  -----------------  ---------  ------------------  ---------


Variable:  Agency
------------------  -----------------  ------------------  ---------------  --------------  ------------------  -------------
mean                std                min                 25%              50%             75%                 max
312.17598279059087  81.46951466502284  178.45050472300002  225.25822262375  326.0807865845  387.47152918099994  420.069427183
------------------  -----------------  ------------------  ---------------  --------------  ------------------  -------------


Variable:  Non-Agency
-----------------  ------------------  ------------------  -----------------  ----------  -----------  ---------
mean               std                 min                 25%                50%         75%          max
19.75995772727273  14.387686260230364  3.1360460000000003  9.679806250000002  15.9661695  27.37888975  61.359347
-----------------  ------------------  ------------------  -----------------  ----------  -----------  ---------


Variable:  Total
-----------------  -----------------  ------------------  ------------------  --------------  ------------------  ------------------
mean               std                min                 25%                 50%             75%                 max
331.9359405178637  85.86526761778086  196.00467972300004  250.16173567000004  334.8768530845  403.61171518099997  465.80257386999995
-----------------  -----------------  ------------------  ------------------  --------------  ------------------  ------------------


Variable:  MBS
----------------  -----------------  ------  ------  ------  ------  --------
mean              std                min     25%     50%     75%     max
7895.15288888889  475.3055464384053  7324.8  7514.3  7809.8  8181.4  8709.776
----------------  -----------------  ------  ------  ------  ------  --------


Variable:  CMO
------------------  ------------------  ------  ------  ------  ------------------  ------
mean                std                 min     25%     50%     75%                 max
1228.3337869666668  137.86582062540757  1110.6  1111.5  1117.4  1374.8040827000002  1395.3
------------------  ------------------  ------  ------  ------  ------------------  ------


Variable:  CMBS
-----------------  -----------------  --------  -------  -----------------  --------  -----------------
mean               std                min       25%      50%                75%       max
582.7686288287373  22.05535928754759  547.0963  561.593  595.7968000000001  596.3996  602.0233000000001
-----------------  -----------------  --------  -------  -----------------  --------  -----------------


Variable:  RMBS
-----------------  -----------------  --------  -------  --------  --------  --------
mean               std                min       25%      50%       75%       max
788.0963677395641  21.23248041333527  751.0353  781.309  783.7491  803.1273  820.0495
-----------------  -----------------  --------  -------  --------  --------  --------


Variable:  Agency
-----------------  -----------------  ------  ------  ------  ------  -------------
mean               std                min     25%     50%     75%     max
9123.486675855554  600.3287561583282  8435.4  8626.6  8921.3  9559.6  10084.5800827
-----------------  -----------------  ------  ------  ------  ------  -------------


Variable:  Non-Agency
------------------  ------------------  ---------  ---------  ------------------  ---------  ---------
mean                std                 min        25%        50%                 75%        max
1370.8649965683014  16.461958530157435  1346.8321  1364.7203  1367.1711691147132  1380.1487  1401.5336
------------------  ------------------  ---------  ---------  ------------------  ---------  ---------


Variable:  Total
------------------  -----------------  ---------  ---------  ----------  ----------  ------------------
mean                std                min        25%        50%         75%         max
10494.351672423858  600.9761265253999  9802.5458  9991.3203  10295.5445  10906.4321  11451.751251814714
------------------  -----------------  ---------  ---------  ----------  ----------  ------------------

<Axes: xlabel='0', ylabel='0'>

• Agency and MBS issuance follow the same pattern. The outstanding increases overall.

• There doesn't appear to be a pattern related to the macro economic factors.

• RMBS is negatively associated with the other MBS outstanding issuances.

ABS ISSUANCE & OUTSTANDING

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Asset-Backed-Securities-Statistics-SIFMA.xlsx"
#Issuance
ABS_issuance = process(path, "ABS Issuance", 9, 7)
ABS_issuance = ABS_issuance.iloc[25:,]
plt.figure(figsize=(18, 10))
plot_dataframe(ABS_issuance,"ABS Issuance","Month","Amount in Billions")


#Outstanding
ABS_outstanding = process(path, "ABS Outstanding", 13, 7)
ABS_outstanding = ABS_outstanding.iloc[38:,]
plt.figure(figsize=(18, 10))
plot_dataframe(ABS_outstanding,"ABS Outstanding","Month","Amount in Billions")

descriptive_stats(ABS_issuance)
descriptive_stats(ABS_outstanding)

ABS = pd.concat([ABS_outstanding, ABS_issuance], axis = 1)
sns.heatmap(ABS.corr())

Variable:  Auto
------------------  -----------------  -----------------  --------  --------  ---------  ---------
mean                std                min                25%       50%       75%        max
10260.394524769079  4184.672656184976  749.9780000000001  7935.316  10438.37  12901.009  17270.505
------------------  -----------------  -----------------  --------  --------  ---------  ---------


Variable:  CDO/CLO
------------------  -----------------  -----  -------  ------------------  --------  ------------------
mean                std                min    25%      50%                 75%       max
1549.4693173076923  938.6032305255599  500.5  880.576  1515.7000000000003  1965.464  4093.8019999999997
------------------  -----------------  -----  -------  ------------------  --------  ------------------


Variable:  Credit Cards
------------------  ------------------  ---  -------  ------------------  ------------------  --------
mean                std                 min  25%      50%                 75%                 max
1719.3950206892307  1443.6092196992554  0    217.916  1899.8090000000002  2105.7700000000004  4296.149
------------------  ------------------  ---  -------  ------------------  ------------------  --------


Variable:  Equipment
------------------  ------------------  -------  -----  --------  ------------------  --------
mean                std                 min      25%    50%       75%                 max
1777.4029901358465  1118.7952650814216  150.321  850.0  1752.878  2379.1549999999997  4230.581
------------------  ------------------  -------  -----  --------  ------------------  --------


Variable:  Other
----------------  ----------------  --------  --------  --------  -----------------  ---------
mean              std               min       25%       50%       75%                max
4385.73556560623  2577.69966285496  2250.831  3030.562  3515.801  4829.179999999999  11639.919
----------------  ----------------  --------  --------  --------  -----------------  ---------


Variable:  Student Loans
------------------  ------------------  ---  ---  ---  -----------  -------
mean                std                 min  25%  50%  75%          max
230.13991125384615  286.29289076389034  0    0.0  0.0  567.8808463  717.192
------------------  ------------------  ---  ---  ---  -----------  -------


Variable:  Total
------------------  ------------------  --------  ------------------  ------------------  ------------------  ------------------
mean                std                 min       25%                 50%                 75%                 max
19922.537329761926  5589.4454044827025  5043.275  18702.326999999997  20881.713999999996  23770.241286905002  26803.498999999996
------------------  ------------------  --------  ------------------  ------------------  ------------------  ------------------


Variable:  Automobile
------------------  -----------------  ------------------  ------------------  ------------------  -----------------  ------------------
mean                std                min                 25%                 50%                 75%                max
221.74933166087115  9.899505843931735  205.75166227689604  216.40263758531776  220.47796096595292  229.4523672028691  236.45433126992148
------------------  -----------------  ------------------  ------------------  ------------------  -----------------  ------------------


Variable:  Credit Card
----------------  -----------------  -----------------  -----------------  -----------------  ------------------  ------------------
mean              std                min                25%                50%                75%                 max
80.9797087252782  23.74913339390182  53.89912201331603  59.06653868119966  77.59494414861933  102.61386577404795  119.36591066197806
----------------  -----------------  -----------------  -----------------  -----------------  ------------------  ------------------


Variable:  Equipment
----------------  -----------------  -----------------  -----------------  -----------------  -----------------  -----------------
mean              std                min                25%                50%                75%                max
72.0092807579709  5.110924460085022  62.85453279413162  67.91153174365925  74.08711302460296  74.49447459076279  80.20064215588268
----------------  -----------------  -----------------  -----------------  -----------------  -----------------  -----------------


Variable:  Other
----------------  ------------------  ------------------  ------------------  -----------------  -----------------  ------------------
mean              std                 min                 25%                 50%                75%                max
226.640977080711  14.770439173961675  209.00125956136372  221.03026098557046  222.2311624838141  229.2797038012255  265.45723464214575
----------------  ------------------  ------------------  ------------------  -----------------  -----------------  ------------------


Variable:  Student Loans
------------------  ---------------  ------------------  ------------------  ------------------  ------------------  ------------------
mean                std              min                 25%                 50%                 75%                 max
152.94934020822691  8.1118561499861  141.37581901080378  145.99426548381834  153.62142975940208  159.26743795973334  166.32837770085686
------------------  ---------------  ------------------  ------------------  ------------------  ------------------  ------------------


Variable:  CDO/CLO
-----------------  ------------------  -----------------  ---------------  --------------  -----------------  -----------------
mean               std                 min                25%              50%             75%                max
806.7731523959842  31.385585408000058  754.8442242624084  791.36805529962  807.9834323046  828.7988801707309  859.1831716646078
-----------------  ------------------  -----------------  ---------------  --------------  -----------------  -----------------


Variable:  Total
------------------  ------------------  ------------------  ------------------  ----------------  -----------------  ------------------
mean                std                 min                 25%                 50%               75%                max
1561.1017908290423  51.462696462734556  1475.5833753669172  1530.2953573308398  1559.24984625271  1588.884313614794  1663.1961190365641
------------------  ------------------  ------------------  ------------------  ----------------  -----------------  ------------------

<Axes: xlabel='0', ylabel='0'>

• Auto issuance follows a different pattern than the other asset backed securities. All the asset backed securities dropped at the beginning of the year.
• No obvious patterns with the macro economic indicators.

FIXED INCOME ISSUANCE & OUTSTANDING

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Fixed-Income-Securities-Statistics-SIFMA.xlsx"
#Issuance
fixed_issuance = process(path, "Issuance", 6, 7)
fixed_issuance = fixed_issuance.iloc[26:,]
plt.figure(figsize=(18, 10))
plot_dataframe(fixed_issuance,"Fixed Issuance","Month","Amount in Billions")


#Outstanding
fixed_outstanding = process(path, "Outstanding", 6, 8)
fixed_outstanding = fixed_outstanding.iloc[12:,]
plt.figure(figsize=(18, 10))
plot_dataframe(fixed_outstanding,"Fixed Outstanding","Month","Amount in Billions")

descriptive_stats(fixed_issuance)
descriptive_stats(fixed_outstanding)

fixed = pd.concat([fixed_issuance, fixed_outstanding], axis=1)
sns.heatmap(fixed.corr())

Variable:  UST
------------------  ------------------  ------------------  -------------  ------------  -------------  -----------------
mean                std                 min                 25%            50%           75%            max
302.78196441666665  124.51234403437519  116.75441869999999  262.000297725  265.00032085  347.925469875  531.8714828000001
------------------  ------------------  ------------------  -------------  ------------  -------------  -----------------


Variable:  MBS
------------------  ------------------  ------------  -------------  -------------  --------------  ------------------
mean                std                 min           25%            50%            75%             max
115.51905391941666  22.500385193671818  86.261279776  99.4752825645  113.752399434  124.7607253715  157.61898723100003
------------------  ------------------  ------------  -------------  -------------  --------------  ------------------


Variable:  Corporates
------------------  -----------------  ------------------  -----------------  --------  ----------  --------
mean                std                min                 25%                50%       75%         max
114.33003333333335  46.33864134407861  13.411999999999999  92.99647499999999  111.2308  132.821075  181.4165
------------------  -----------------  ------------------  -----------------  --------  ----------  --------


Variable:  Munis
------------------  -----------------  ------------------  ------------------  ------------------  -------  -------
mean                std                min                 25%                 50%                 75%      max
29.319091666666665  6.693330181804235  20.123199999999997  25.637100000000004  27.990650000000002  33.3985  41.7301
------------------  -----------------  ------------------  ------------------  ------------------  -------  -------


Variable:  Agency
------------------  -----------------  ---------  -----------  ---------  ------------------  ---------
mean                std                min        25%          50%        75%                 max
111.61536333333333  60.01884547742587  53.223965  74.11392375  100.03921  119.14523874999999  268.34616
------------------  -----------------  ---------  -----------  ---------  ------------------  ---------


Variable:  ABS
------------------  -----------------  ------------------  ----------  ----------  ---------  ------------------
mean                std                min                 25%         50%         75%        max
19.601895333333335  5.711744184620559  5.0432749999999995  18.1998545  20.2375355  22.200997  26.803498999999995
------------------  -----------------  ------------------  ----------  ----------  ---------  ------------------


Variable:  Total
-----------------  ------------------  ------------  ---------------  -----------------  -------------  --------------
mean               std                 min           25%              50%                75%            max
693.1674020027499  188.61275883407333  355.55877728  612.96483273675  677.4537293994999  804.476946148  1014.553359231
-----------------  ------------------  ------------  ---------------  -----------------  -------------  --------------


Variable:  UST
------------------  -----------------  ---------  ---------  -----------------  -----------------  -----------------
mean                std                min        25%        50%                75%                max
23295.411859811575  1073.589091626232  21732.973  22584.039  23306.73509207125  23934.45280527553  24881.15746614197
------------------  -----------------  ---------  ---------  -----------------  -----------------  -----------------


Variable:  MBS
------------------  ------------------  ------------------  ------------------  ------------------  ------------------  -----------------
mean                std                 min                 25%                 50%                 75%                 max
11926.049472081484  265.89181935904526  11671.039213308586  11788.260116631065  11905.481019953544  12053.554601467931  12201.62818298232
------------------  ------------------  ------------------  ------------------  ------------------  ------------------  -----------------


Variable:  Corporates
------------  -----------------  ---------  ----------  ------------------  ------------------  ------------------
mean          std                min        25%         50%                 75%                 max
10131.413125  114.2818946533244  10011.796  10054.2965  10099.383999999998  10174.585500000001  10358.618999999999
------------  -----------------  ---------  ----------  ------------------  ------------------  ------------------


Variable:  Munis
------------------  ------------------  -------  ---------  ------------------  ----------  --------
mean                std                 min      25%        50%                 75%         max
4044.1421250000003  19.074616434544605  4015.56  4034.1825  4046.8140000000003  4056.86575  4069.432
------------------  ------------------  -------  ---------  ------------------  ----------  --------


Variable:  Agency
-----------------  -----------------  -----------  ------------------  -----------  -----------  -----------
mean               std                min          25%                 50%          75%          max
1729.161201777778  309.2418582559171  1433.292829  1466.8459440000001  1619.225243  1935.749527  2257.040729
-----------------  -----------------  -----------  ------------------  -----------  -----------  -----------


Variable:  ABS
----------------  -----------------  ------------------  ------------------  ------------------  ------------------  -----------------
mean              std                min                 25%                 50%                 75%                 max
1525.17328900103  55.58148135072386  1475.5833753669172  1495.1340177523327  1514.6846601377483  1549.9682458180866  1585.251831498425
----------------  -----------------  ------------------  ------------------  ------------------  ------------------  -----------------


Variable:  MMs
------------------  ------------------  ------------  -------------  ------------------  ------------  ------------
mean                std                 min           25%            50%                 75%           max
1105.0251099322222  43.879421915727505  1014.1696392  1085.40111239  1121.3795476999999  1128.3443019  1166.1017535
------------------  ------------------  ------------  -------------  ------------------  ------------  ------------


Variable:  Total
-----------------  -----------------  ------------------  ------------------  ------------------  ----------------  -----------------
mean               std                min                 25%                 50%                 75%               max
52132.96692217918  719.6096566942415  51578.757634836344  51726.330141928396  51873.902649020456  52410.0715658506  52946.24048268075
-----------------  -----------------  ------------------  ------------------  ------------------  ----------------  -----------------

<Axes: xlabel='0', ylabel='0'>

• UST follows a more exagerated pattern than the other fixed issuance.
• There doesn't seem to be an association with the macro economic factors.

US ABCP & CP Outstanding

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\ABCP and OP.xls"
#Issuance
paper_outstanding = process(path, "CP Outstanding", 2, 5)
paper_outstanding = paper_outstanding.iloc[25:,]
plt.figure(figsize=(18, 10))
plot_dataframe(paper_outstanding,"Commercial Paper Outstanding","Month","Amount in Billions")

descriptive_stats(paper_outstanding)
sns.heatmap(paper_outstanding.corr())

Variable:  Non-Financial
------------------  ------------------  ---------  ------------  -----------  ------------  ---------
mean                std                 min        25%           50%          75%           max
223.11836644081646  63.990540299670755  92.899368  174.49379366  226.1481895  274.71558705  347.49606
------------------  ------------------  ---------  ------------  -----------  ------------  ---------


Variable:  Financial
-----------------  -----------------  -------------  ------------------  -------------  ------------  -------------
mean               std                min            25%                 50%            75%           max
551.7777289340193  96.46621378536781  409.288579877  498.20518843300005  524.905803442  562.21009502  850.809960306
-----------------  -----------------  -------------  ------------------  -------------  ------------  -------------


Variable:  ABCP
------------------  -----------------  ----------  ------------  ------------  -------------  ------------
mean                std                min         25%           50%           75%            max
369.30349115797475  204.6936592133436  223.594487  243.78553525  263.09429755  397.438735415  1189.4634866
------------------  -----------------  ----------  ------------  ------------  -------------  ------------


Variable:  Other
-------------------  -------------------  ---  ---  ---  ---  ---------
mean                 std                  min  25%  50%  75%  max
0.07217620506329113  0.40622656129680923  0    0.0  0.0  0.0  3.6294701
-------------------  -------------------  ---  ---  ---  ---  ---------


Variable:  Total
------------------  -----------------  -------------  ------------------  ---------------  ---------------  --------------
mean                std                min            25%                 50%              75%              max
1144.2717627378731  266.9932289063738  884.870209877  1006.2110876842501  1050.5643060855  1120.3176276325  2160.725459285
------------------  -----------------  -------------  ------------------  ---------------  ---------------  --------------

<Axes: xlabel='0', ylabel='0'>

• Financial and Non-financial CP outstanding follow a simliar path.
• ABCP is negatively correlated with the others.

US MUNICIPAL ISSUANCE

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Municipal-Bonds-Statistics-SIFMA.xlsx"

municipal_bond_type = process(path, "Issuance Total", 8, 2)
municipal_bond_type = municipal_bond_type.iloc[42:,]
municipal_bond_type.columns = ["GO", "Revenue"]

municipal_bid_type = process(path, "Issuance Total", 8, 6)
municipal_bid_type = municipal_bid_type.iloc[42:, 3: ]
municipal_bid_type.columns = ["Competitive", "Negotiated", "Private Placement"]

municipal_capital = process(path, "Issuance Total", 8, 9)
municipal_capital = municipal_capital.iloc[42:, 7:]
municipal_capital.columns = ["New Capital", "Refunding"]

municipal_total = process(path, "Issuance Total", 8, 11)
municipal_total = municipal_total.iloc[42:, 10:]
municipal_total.columns = ["Total"]

municipal_issuance = pd.concat([municipal_bond_type, municipal_bid_type, municipal_capital, municipal_total], axis = 1)

plt.figure(figsize=(18, 10))
plot_dataframe(municipal_issuance,"Municipal Issuance","Month","Amount in Billions")


sns.heatmap(municipal_issuance.corr())

<Axes: >

• Municipal issuance follows a strong pattern. there are drops in april 2020 and december 2020.
• Privated placement not correlated with the others.
• The drop in april 2020 is around the same time the unemployment rate spiked during the pandemic.

Corporate Issuance

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\sifma\US-Corporate-Bonds-Statistics-SIFMA.xlsx"
corporate_issuance_3 = process(path, "Issuance", 7, 3)
corporate_issuance_3 = corporate_issuance_3.iloc[25:,]
corporate_issuance_5 = process(path, "Issuance", 7, 6)
corporate_issuance_5 = corporate_issuance_5.iloc[25:, 4:]

corporate_issuance_8 = process(path, "Issuance", 7, 9)
corporate_issuance_8 = corporate_issuance_8.iloc[25:, 7:]
corporate_issuance = pd.concat([corporate_issuance_3, corporate_issuance_5, corporate_issuance_8], axis = 1)

plt.figure(figsize=(18, 10))
plot_dataframe(corporate_issuance,"Corporate Bond Issuance","Month","Amount in Billions")

descriptive_stats(corporate_issuance)
sns.heatmap(corporate_issuance.corr())

Variable:  Investment Grade
-----------------  -----------------  ------------------  -------  ------------------  ------------------  ---------
mean               std                min                 25%      50%                 75%                 max
99.26772846153844  40.98405887104214  10.623899999999999  82.9061  100.98734999999999  107.63749999999999  159.62528
-----------------  -----------------  ------------------  -------  ------------------  ------------------  ---------


Variable:  High Yield
------------------  -----------------  ------  -----------------  ------------------  ------------------  --------
mean                std                min     25%                50%                 75%                 max
11.439316153846153  6.673691399385024  2.2321  6.726109999999999  10.464870000000001  15.157950000000001  21.82498
------------------  -----------------  ------  -----------------  ------------------  ------------------  --------


Variable:  Total
------------------  ------------------  ------------------  -------  --------  --------  ---------
mean                std                 min                 25%      50%       75%       max
110.70704461538459  45.630712608477424  12.855999999999998  90.8836  107.9476  118.0804  181.33989
------------------  ------------------  ------------------  -------  --------  --------  ---------


Variable:  Callable
-----------------  ------------------  ------  -------  -------  ------------------  ---------
mean               std                 min     25%      50%      75%                 max
97.17755615384614  42.415898938335715  9.9315  75.8803  96.4189  104.38380000000001  177.33718
-----------------  ------------------  ------  -------  -------  ------------------  ---------


Variable:  Non-Callable
-----------------  -----------------  --------  --------  -------  ------  -------
mean               std                min       25%       50%      75%     max
6.132448846153846  5.448054444156568  1.030271  1.690039  5.00798  7.7414  16.7314
-----------------  -----------------  --------  --------  -------  ------  -------


Variable:  Fixed Rate
-----------------  -----------------  -----------------  ------  -----------------  --------  ----------
mean               std                min                25%     50%                75%       max
86.47021884615387  42.19876075645729  8.405999999999999  60.723  82.94860000000001  105.9304  162.767451
-----------------  -----------------  -----------------  ------  -----------------  --------  ----------


Variable:  Floating Rate
------------------  -----------------  ------  --------  -------  -----  ------------------
mean                std                min     25%       50%      75%    max
16.839786153846152  9.872238132632793  3.5495  10.52873  15.5641  23.85  31.932810000000003
------------------  -----------------  ------  --------  -------  -----  ------------------

<Axes: xlabel='0', ylabel='0'>

• Investment grade, calleable and fixed rate follow the same pattern, and we can see that they are highly correlated.
• We see spikes every two months.

Discussion of the issuance and outstanding for each market against the trading volume in that market (of comparable markets)

Treasury

• Treasury issuance is highly correlated with coupon securities trading volume. this mease that the treasuries issued were coupon securities.

Fixed Income

• Comparing the trading volume of fixed income and the issuance tells us that UST are the most issued, while agency follows behind.

Corporate Bond

• The total trading volume of corporate bond issuance shows that Investment grade, callable, and fixed rate bonds are issued and traded the most.

Top 10 Token Analysis

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\xrp-usd-max.xls"
xrp = pd.read_csv(path)
xrp['snapped_at'] = pd.to_datetime(xrp['snapped_at'], utc=True)
xrp.set_index('snapped_at', inplace = True)
plot_dataframe(xrp[['total_volume']], "XRP", "Date", "Val")
plot_dataframe(xrp[['price']], "Price Data", "Date", "Price")

• XRP (XRP) is a cryptocurrency that is part of the Ripple network, which is designed for facilitating fast and low-cost cross-border payments.
• The price jumped in 2018, but the volume didn't increase until 2021, after which, the price begain to fall.

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\usdt-usd-max.xls"
usdt = pd.read_csv(path)
usdt['snapped_at'] = pd.to_datetime(usdt['snapped_at'], utc=True)
usdt.set_index('snapped_at', inplace = True)
plot_dataframe(usdt[['total_volume']], "USDT", "Date", "Val")
plot_dataframe(usdt[['price']], "Price Data", "Date", "Price")

• USDT is the ticker symbol for Tether (USDT), one of the most well-known and widely used stablecoins in the cryptocurrency market. Stablecoins are a type of cryptocurrency that aims to maintain a stable value by pegging their price to a reserve of assets, often a fiat currency like the US Dollar (USD). In the case of USDT, it is designed to maintain a 1:1 value ratio with the US Dollar.

• Total volume has decreased as CPI and inflation have increased

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\trx-usd-max.xls"
trx = pd.read_csv(path)
trx['snapped_at'] = pd.to_datetime(trx['snapped_at'], utc=True)
trx.set_index('snapped_at', inplace = True)
plot_dataframe(trx[['total_volume']], "TRX", "Date", "Val")

plot_dataframe(trx[['price']], "Price Data", "Date", "Price")

• TRX is the ticker symbol for Tron (TRX), a blockchain-based cryptocurrency and platform designed for decentralized applications (DApps) and digital content sharing.

• The price follows a similar path as the volume, but has increased in 2023 as CPI and inflation have increased.

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\steth-usd-max.xls"
steth = pd.read_csv(path)
steth['snapped_at'] = pd.to_datetime(steth['snapped_at'], utc=True)
steth.set_index('snapped_at', inplace = True)
plot_dataframe(steth[['total_volume']], "STETH", "Date", "Val")

plot_dataframe(steth[['price']], "Price Data", "Date", "Price")

• stETH refers to a synthetic asset representing staked Ether (ETH) on the Ethereum 2.0 Beacon Chain.
• For this crypto, the price doesn't seem to have anything to do with the total volume.
• Price doesn't follow the path of inflation

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\sol-usd-max.xls"
sol = pd.read_csv(path)
sol['snapped_at'] = pd.to_datetime(sol['snapped_at'], utc=True)
sol.set_index('snapped_at', inplace = True)
plot_dataframe(sol[['total_volume']], "SOL", "Date", "Val")

plot_dataframe(sol[['price']], "SOL Price Data", "Date", "Price")

• SOL refers to the native cryptocurrency of the Solana blockchain, known as the Solana (SOL) token. Solana is a high-performance blockchain platform designed for decentralized applications (DApps) and cryptocurrencies. It aims to provide fast and scalable blockchain solutions to support various decentralized applications and projects.
• The price spikes around the same time the total volume spikes, but doesn't decrease as rapidly.

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\eth-usd-max.xls"
eth = pd.read_csv(path)
eth['snapped_at'] = pd.to_datetime(eth['snapped_at'], utc=True)
eth.set_index('snapped_at', inplace = True)
plot_dataframe(eth[['total_volume']], "ETH", "Date", "Val")

plot_dataframe(eth[['price']], "ETH Price Data", "Date", "Price")

• ETH refers to Ethereum (ETH), which is one of the most well-known and widely used cryptocurrencies in the world. Ethereum is a blockchain platform that enables the creation of decentralized applications (DApps) and the issuance of smart contracts.
• the price of ETH follows the total volume

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\doge-usd-max.xls"
doge = pd.read_csv(path)
doge['snapped_at'] = pd.to_datetime(doge['snapped_at'], utc=True)
doge.set_index('snapped_at', inplace = True)
plot_dataframe(doge[['total_volume']], "Doge", "Date", "Val")

plot_dataframe(doge[['price']], "Doge Price Data", "Date", "Price")

• DOGE refers to Dogecoin, which is a cryptocurrency that started as a joke but has gained significant popularity and a dedicated community since its creation in December 2013.

• The price of doge spiked in 2021 along with the total volume, and has since steadily fallen.

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\btc-usd-max.xls"
btc = pd.read_csv(path)
btc['snapped_at'] = pd.to_datetime(btc['snapped_at'], utc=True)
btc.set_index('snapped_at', inplace = True)
plot_dataframe(btc[['total_volume']], "BTC", "Date", "Val")

plot_dataframe(btc[['price']], "BTC Price Data", "Date", "Price")

• Bitcoin is a cryptocurrency or digital currency. It is the first and most well-known cryptocurrency, created by an anonymous entity or individual using the pseudonym Satoshi Nakamoto.
• THe price of bitcoin skyrocketed following 2021, is mostly a good reflection of the total volume.

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\bnb-usd-max.xls"
bnb = pd.read_csv(path)
bnb['snapped_at'] = pd.to_datetime(bnb['snapped_at'], utc=True)
bnb.set_index('snapped_at', inplace = True)
plot_dataframe(bnb[['total_volume']], "BNB", "Date", "Val")

plot_dataframe(bnb[['price']], "BNB Price Data", "Date", "Price")

• BNB, short for Binance Coin, is the native cryptocurrency of the Binance cryptocurrency exchange, one of the largest and most well-known cryptocurrency exchanges in the world.
• the price of BNB increased in 2021, as did the total volume. The price decreased with the total volume, but remains high.

path = r"C:\Users\mhlad\OneDrive\Desktop\Georgia Tech\Chava\Homework 2\crypto\ada-usd-max.xls"
ada = pd.read_csv(path)
ada['snapped_at'] = pd.to_datetime(ada['snapped_at'], utc=True)
ada.set_index('snapped_at', inplace = True)
plot_dataframe(ada[['total_volume']], "ADA", "Date", "Val")

plot_dataframe(ada[['price']], "ADA Price Data", "Date", "Price")

• "ADA" refers to Cardano's native cryptocurrency, Ada (ADA). Cardano is a blockchain platform designed for the development of decentralized applications (DApps) and smart contracts.
• The price of ADA was high when it was first introduced, but has increased in 2021.

Interestingly, overall, the prices of the tokens have jumped in 2021. This could be due to a number of reasons, including increased adoption, institutional invesment, and Low Interest rates. As interest rates have increased, the prices have lowered.

Overall Analysis There appears to be a recurring pattern within the fixed income sector, showing distinct seasonality. In the early part of the year, especially during tax season, we observe a surge in the short-term borrowing needs in the United States. This phenomenon is likely tied to increased demand for Treasury Securities, resulting in a boost in their supply. Consequently, this surplus of supply tends to push yields higher, rendering them more enticing compared to holding cash.

As we approach the end of the year, typically around September and October, there is a noticeable shift in investment preferences towards equities. This shift can be attributed to the portfolio rebalancing that occurs as investors transition their assets from fixed income to equities, possibly in preparation for the coming year. The key insight for the cryptocurrency market is that it remains susceptible to broader market dynamics. Despite assertions from its proponents suggesting otherwise, the crypto market is not immune to the influence of macroeconomic indicators.